At form time step 2, because of the better results, he uses the 2nd batch of gradients as the input of the 3rd step as the cost function is better optimized.
I’d like to use the 2nd batch of gradients as the cost function of step 2 only. Any idea how to do it? Or should I re-train the network multiple times to achieve this?
The reason why you don’t want to train your network twice is because of how the optimization functions work:
If you are using SGD or ADAM, or any other gradient descent optimization methods, your loss values will decrease as the cost function is better optimized.
Also, if you have a batch size of two you are going to experience the issues you are having, because the network will get biased due to the batch size and just get stuck in a local minima.
You would want to use a strategy similar to this:
Train a first time using a batch size of 10
get a loss value
Stop the training and save the weights.
Train for another time using a batch size of two.
Increase the batch size.
Repeat step 2 and 3 (e.g. 50 times)
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